Python实现朴素贝叶斯算法——屏蔽社区留言板的侮辱性言论
# -*- coding:utf-8 -*-
import numpy as np
__author__ = 'yangxin'
"""
贝叶斯公式
p(xy)=p(x|y)p(y)=p(y|x)p(x)
p(x|y)=p(y|x)p(x)/p(y)
"""
"""
屏蔽社区留言板的侮辱性言论
"""
class SpeechJudgment(object):
def load_data_set(self):
# 单词列表
posting_list = [
['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
['stop', 'posting', 'stupid', 'worthless', 'gar e'],
['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
# 属性类别列表 1 -> 侮辱性的文字, 0 -> not
class_vec = [0, 1, 0, 1, 0, 1]
return posting_list, class_vec
def create_vocab_list(self, data_set):
vocab_set = set()
for item in data_set:
vocab_set = vocab_set | set(item)
# 不含重复元素的单词列表
return list(vocab_set)
def set_of_words2vec(self, vocab_list, input_set):
result = [0] * len(vocab_list)
for word in input_set:
if word in vocab_list:
# 如单词在输入文档出现过，则标记为1，否则为0
result[vocab_list.index(word)] = 1
return result
def train_naive_bayes(self, train_mat, train_category):
train_doc_num = len(train_mat)
words_num = len(train_mat[0])
pos_abusive = np.sum(train_category) / train_doc_num
# 创建一个长度为words_num的都是1的列表
p0num = np.ones(words_num)
p1num = np.ones(words_num)
p0num_all = 2.0
p1num_all = 2.0
for i in range(train_doc_num):
if train_category[i] == 1:
p1num += train_mat[i]
p1num_all += np.sum(train_mat[i])
else:
p0num += train_mat[i]
p0num_all += np.sum(train_mat[i])
p1vec = np.log(p1num / p1num_all)
p0vec = np.log(p0num / p0num_all)
return p0vec, p1vec, pos_abusive
def classify_naive_bayes(self, vec_to_classify, p0vec, p1vec, p_class1):
p1 = np.sum(vec_to_classify * p1vec) + np.log(p_class1)
p0 = np.sum(vec_to_classify * p0vec) + np.log(1 - p_class1)
if p1 > p0:
return 1
else:
return 0
def bag_words_to_vec(self, vocab_list, input_set):
result = [0] * len(vocab_list)
for word in input_set:
if word in vocab_list:
result[vocab_list.index(word)] += 1
else:
print('the word: {} is not in my vocabulary'.format(word))
return result
def testing_naive_bayes(self):
list_post, list_classes = self.load_data_set()
vocab_list = self.create_vocab_list(list_post)
train_mat = []
for post_in in list_post:
train_mat.append(
self.set_of_words_to_vec(vocab_list, post_in)
)
p0v, p1v, p_abusive = self.train_naive_bayes(np.array(train_mat), np.array(list_classes))
test_one = ['love', 'my', 'dalmation']
test_one_doc = np.array(self.set_of_words2vec(vocab_list, test_one))
print('the result is: {}'.format(self.classify_naive_bayes(test_one_doc, p0v, p1v, p_abusive)))
test_two = ['stupid', 'garbage']
test_two_doc = np.array(self.set_of_words2vec(vocab_list, test_two))
print('the result is: {}'.format(self.classify_naive_bayes(test_two_doc, p0v, p1v, p_abusive)))
